SELDI-TOF-MS of saliva: methodology and pre-treatment effects

Interest in saliva as a diagnostic fluid for monitoring general health and for early diagnosis of disease has increased in the last few years. In particular, efforts have focused on the generation of protein maps of saliva using advanced proteomics technology. Surface-enhanced laser-desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is a novel high throughput and extremely sensitive proteomic approach that allows protein expression profiling of large sets of complex biological specimens. In this study, large scale profiling of salivary proteins and peptides, ranging from 2 to 100kDa was demonstrated using SELDI-TOF-MS. Various methodological aspects and pre-analytical variables were analysed with respect to their effects on saliva SELDI-TOF-MS profiling. Results show that chip surface type and sample type (unstimulated versus stimulated) critically affect the amount and composition of detected salivary proteins. Factors that influenced normal saliva protein profiling were matrix composition, sample dilution and binding buffer properties. Delayed processing time experiments show certain new peptides evolving 3h post-saliva donation, and quantitative analyses indicate relative intensity of other proteins and peptides changing with time. The addition of protease inhibitors partly counteracted the destabilization of certain protein/peptide mass spectra over time suggesting that some proteins in saliva are subject to digestion by intrinsic salivary proteases. SELDI-TOF-MS profiles also changed by varying storage time and storage temperature whereas centrifugation speed and freeze-thaw cycles had minimal impact. In conclusion, SELDI-TOF-MS offers a high throughput platform for saliva protein and peptide profiling, however, (pre-)analytical conditions must be taken into account for valid interpretation of the acquired data.     

Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution

Biomarker discovery produces lists of candidate markers whose presence and level must be subsequently verified in serum or plasma. Verification represents a paradigm shift from unbiased discovery approaches to targeted, hypothesis-driven methods and relies upon specific, quantitative assays optimized for the selective detection of target proteins. Many protein biomarkers of clinical currency are present at or below the nanogram/milliliter range in plasma and have been inaccessible to date by MS-based methods. Using multiple reaction monitoring coupled with stable isotope dilution mass spectrometry, we describe here the development of quantitative, multiplexed assays for six proteins in plasma that achieve limits of quantitation in the 1-10 ng/ml range with percent coefficients of variation from 3 to 15% without immunoaffinity enrichment of either proteins or peptides. Sample processing methods with sufficient throughput, recovery, and reproducibility to enable robust detection and quantitation of candidate biomarker proteins were developed and optimized by addition of exogenous proteins to immunoaffinity depleted plasma from a healthy donor. Quantitative multiple reaction monitoring assays were designed and optimized for signature peptides derived from the test proteins. Based upon calibration curves using known concentrations of spiked protein in plasma, we determined that each target protein had at least one signature peptide with a limit of quantitation in the 1-10 ng/ml range and linearity typically over 2 orders of magnitude in the measurement range of interest. Limits of detection were frequently in the high picogram/milliliter range. These levels of assay performance represent up to a 1000-fold improvement compared with direct analysis of proteins in plasma by MS and were achieved by simple, robust sample processing involving abundant protein depletion and minimal fractionation by strong cation exchange chromatography at the peptide level prior to LC-multiple reaction monitoring/MS. The methods presented here provide a solid basis for developing quantitative MS-based assays of low level proteins in blood.     

Effects of preanalytical variables on peptide and protein measurements in human serum and plasma: implications for clinical proteomics

There is a wealth of knowledge in the field of in vitro diagnostics with regard to preanalytical variables and their impact on the determination of peptide and protein analytes in human serum and plasma. This information is applicable to clinical proteomics investigations, which utilize the same sample types. Studies have demonstrated that the majority of variations and errors in in vitro diagnostics seem to occur in the preanalytical phase prior to specimen analysis. Preanalytical processes include study design, compliance of the subjects investigated, compliance of the technical staff in adherence to protocols, choice of specimens utilized and sample collection and processing. These variables can have a dramatic impact on the determination of analytes and can affect result outcomes, reproducibility and the validity of investigations. By drawing analogies to in vitro diagnostics practices, specific variables that are likely to impact the results of proteomics studies can be identified. Recognition of such variables is the first step towards their understanding and, eventually, controlling their impact. In this article, we will review preanalytical variables, provide examples for their effects on the determination of distinct peptides and proteins and discuss potential implications for clinical proteomics investigations.     

Methods for peptide and protein quantitation by liquid chromatography-multiple reaction monitoring mass spectrometry

Liquid chromatography-multiple reaction monitoring mass spectrometry of peptides using stable isotope dilution (SID) provides a powerful tool for targeted protein quantitation. However, the high cost of labeled peptide standards for SID poses an obstacle to multiple reaction monitoring studies. We compared SID to a labeled reference peptide (LRP) method, which uses a single labeled peptide as a reference standard for all measured peptides, and a label-free (LF) approach, in which quantitation is based on analysis of un-normalized peak areas for detected MRM transitions. We analyzed peptides from the Escherichia coli proteins alkaline phosphatase and β-galactosidase spiked into lysates from human colon adenocarcinoma RKO cells. We also analyzed liquid chromatography-multiple reaction monitoring mass spectrometry data from a recently published interlaboratory study by the National Cancer Institute Clinical Proteomic Technology Assessment for Cancer network (Addona et al. (2009) Nat. Biotechnol. 27: 633-641), in which unlabeled and isotopically labeled synthetic peptides or their corresponding proteins were spiked into human plasma. SID displayed the highest correlation coefficients and lowest coefficient of variation in regression analyses of both peptide and protein spike studies. In protein spike experiments, median coefficient of variation values were about 10% for SID and 20-30% for LRP and LF methods. Power calculations indicated that differences in measurement error between the methods have much less impact on measured protein expression differences than biological variation. All three methods detected significant (p < 0.05) differential expression of three endogenous proteins in a test set of 10 pairs of human lung tumor and control tissues. Further, the LRP and LF methods both detected significant differences (p < 0.05) in levels of seven biomarker candidates between tumors and controls in the same set of lung tissue samples. The data indicate that the LRP and LF methods provide cost-effective alternatives to SID for many quantitative liquid chromatography-multiple reaction monitoring mass spectrometry applications.     

Evaluating the effects of preanalytical variables on the stability of the human plasma proteome

High quality clinical biospecimens are vital for biomarker discovery, verification, and validation. Variations in blood processing and handling can affect protein abundances and assay reliability. Using an untargeted LC-MS approach, we systematically measured the impact of preanalytical variables on the plasma proteome. Time prior to processing was the only variable that affected the plasma protein levels. LC-MS quantification showed that preprocessing times <6 h had minimal effects on the immunodepleted plasma proteome, but by 4 days significant changes were apparent. Elevated levels of many proteins were observed, suggesting that in addition to proteolytic degradation during the preanalytical phase, changes in protein structure are also important considerations for protocols using antibody depletion. As to processing variables, a comparison of single- vs double-spun plasma showed minimal differences. After processing, the impact ?3 freeze–thaw cycles was negligible regardless of whether freshly collected samples were processed in short succession or the cycles occurred during 14–17 years of frozen storage (−80 °C). Thus, clinical workflows that necessitate modest delays in blood processing times or employ different centrifugation steps can yield valuable samples for biomarker discovery and verification studies.     

液质联用多反应监测法定量目标多肽或蛋白质

为建立优化的血浆内源性多肽提取方法,并且构建目标多肽和蛋白质的质谱定量方 法,本研究考察了超滤法、有机溶剂沉淀法和固相萃取法对血浆内源性多肽的提取效果 ,并通过Tricine-SDS-PAGE对提取效果进行比较.通过液相色谱串联质谱多反应监测 （MRM）分析,建立了多肽标准品ESAT-6定量方法,并将ESAT-6定量建立的液相色谱和质谱条件应用于蛋白质的定量,对多肽和蛋白质MRM定量的标准曲线进行了考 察.Tricine-SDS-PAGE结果表明,乙腈沉淀法是最佳的血浆内源性多肽提取方法,低分子量的多肽可以得到很好的富集,且能有效地去除高分子蛋白质的污染.液相色谱串联 质谱MRM法检测血浆内提取的多肽,标准曲线的线性较好,相关系数为0.999.另外,采 用MRM法对胶内分离的蛋白质进行定量,标准曲线的线性相关系数为0.995.综上所述, 本研究构建了一种简单有效的血浆多肽提取方法,通过液质联用MRM法成功地实现了目标多肽和蛋白质定量测定.该定量方法可以推广应用于复杂样品中的多肽和蛋白质的定 量分析.     

Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics

Selected reaction monitoring (SRM) - also known as multiple reaction monitoring (MRM) - has emerged as a promising high-throughput targeted protein quantification technology for candidate biomarker verification and systems biology applications. A major bottleneck for current SRM technology, however, is insufficient sensitivity for, e.g. detecting low-abundance biomarkers likely present at the low ng/mL to pg/mL range in human blood plasma or serum, or extremely low-abundance signaling proteins in cells or tissues. Herein, we review recent advances in methods and technologies, including front-end immunoaffinity depletion, fractionation, selective enrichment of target proteins/peptides including posttranslational modifications, as well as advances in MS instrumentation which have significantly enhanced the overall sensitivity of SRM assays and enabled the detection of low-abundance proteins at low- to sub-ng/mL level in human blood plasma or serum. General perspectives on the potential of achieving sufficient sensitivity for detection of pg/mL level proteins in plasma are also discussed.     

Multiplexed LC‐MS/MS analysis of horse plasma proteins to study doping in sport

The development of protein biomarkers for the indirect detection of doping in horse is a potential solution to doping threats such as gene and protein doping. A method for biomarker candidate discovery in horse plasma is presented using targeted analysis of proteotypic peptides from horse proteins. These peptides were first identified in a novel list of the abundant proteins in horse plasma. To monitor these peptides, an LC‐MS/MS method using multiple reaction monitoring was developed to study the quantity of 49 proteins in horse plasma in a single run. The method was optimised and validated, and then applied to a population of race‐horses to study protein variance within a population. The method was finally applied to longitudinal time courses of horse plasma collected after administration of an anabolic steroid to demonstrate utility for hypothesis‐driven discovery of doping biomarker candidates.     

Evaluation of large scale quantitative proteomic assay development using peptide affinity-based mass spectrometry

Stable isotope standards and capture by antipeptide antibodies (SISCAPA) couples affinity enrichment of peptides with stable isotope dilution and detection by multiple reaction monitoring mass spectrometry to provide quantitative measurement of peptides as surrogates for their respective proteins. In this report, we describe a feasibility study to determine the success rate for production of suitable antibodies for SISCAPA assays in order to inform strategies for large-scale assay development. A workflow was designed that included a multiplex immunization strategy in which up to five proteotypic peptides from a single protein target were used to immunize individual rabbits. A total of 403 proteotypic tryptic peptides representing 89 protein targets were used as immunogens. Antipeptide antibody titers were measured by ELISA and 220 antipeptide antibodies representing 89 proteins were chosen for affinity purification. These antibodies were characterized with respect to their performance in SISCAPA-multiple reaction monitoring assays using trypsin-digested human plasma matrix. More than half of the assays generated were capable of detecting the target peptide at concentrations of less than 0.5 fmol/μl in human plasma, corresponding to protein concentrations of less than 100 ng/ml. The strategy of multiplexing five peptide immunogens was successful in generating a working assay for 100% of the targeted proteins in this evaluation study. These results indicate it is feasible for a single laboratory to develop hundreds of assays per year and allow planning for cost-effective generation of SISCAPA assays.     

Targeted mass spectrometric approach for biomarker discovery and validation with nonglycosylated tryptic peptides from N-linked glycoproteins in human plasma

A simple mass spectrometric approach for the discovery and validation of biomarkers in human plasma was developed by targeting nonglycosylated tryptic peptides adjacent to glycosylation sites in an N-linked glycoprotein, one of the most important biomarkers for early detection, prognoses, and disease therapies. The discovery and validation of novel biomarkers requires complex sample pretreatment steps, such as depletion of highly abundant proteins, enrichment of desired proteins, or the development of new antibodies. The current study exploited the steric hindrance of glycan units in N-linked glycoproteins, which significantly affects the efficiency of proteolytic digestion if an enzymatically active amino acid is adjacent to the N-linked glycosylation site. Proteolytic digestion then results in quantitatively different peptide products in accordance with the degree of glycosylation. The effect of glycan steric hindrance on tryptic digestion was first demonstrated using alpha-1-acid glycoprotein (AGP) as a model compound versus deglycosylated alpha-1-acid glycoprotein. Second, nonglycosylated tryptic peptide biomarkers, which generally show much higher sensitivity in mass spectrometric analyses than their glycosylated counterparts, were quantified in human hepatocellular carcinoma plasma using a label-free method with no need for N-linked glycoprotein enrichment. Finally, the method was validated using a multiple reaction monitoring analysis, demonstrating that the newly discovered nonglycosylated tryptic peptide targets were present at different levels in normal and hepatocellular carcinoma plasmas. The area under the receiver operating characteristic curve generated through analyses of nonglycosylated tryptic peptide from vitronectin precursor protein was 0.978, the highest observed in a group of patients with hepatocellular carcinoma. This work provides a targeted means of discovering and validating nonglycosylated tryptic peptides as biomarkers in human plasma, without the need for complex enrichment processes or expensive antibody preparations.     

Chemical degradation of 3H-labeled substance P in tris buffer solution

Substance P (SP) is an important neuropeptide that has been implicated in several physiological processes, and it is necessary to devise an analytical procedure to measure endogenous SP with a combination of high sensitivity and maximum molecular specificity. However, the unique chemical nature of SP (polarity, chemical stability, ease of oxidation, peptide bond lability) plays a significant role in its analysis, such as in receptor assays, immunoassays, chromatography, and mass spectrometry. In this study, we evaluated in polypropylene and glass assay tubes the effects on the recovery and stability of tritiated SP ([3H]SP) of several pertinent experimental parameters such as buffer, pH, multiple freeze-thaw cycles, and incubation temperature and time. Bovine serum albumin (BSA) effectively reduced the absorption of [3H]SP to polypropylene and glass tube surfaces. Following multiple (6X) freeze-thaw cycles of solutions in BSA-precoated tubes, the recovery of radioactive [3H]SP remained high (greater than 75%) after the last cycle, whereas recovery was minimal in uncoated or siliconized glass tubes. A high level of radioactivity recovery was maintained for 14 days of storage of [3H]SP in triethylamine formate (TEAF) solution in BSA-precoated tubes at 4 and -20 degrees C, but decreased at 37 degrees C to less than 80% in only 3 h. Following storage in Tris-HCl (pH 7.4) buffer, a combination of HPLC and mass spectrometric analyses revealed that a significant amount of peptide bond cleavage occurred to produce the two peptides ArgProLys (RPK) and ArgProLysProGlnGln (RPKPQQ), with only a small amount of remaining intact SP. That decomposition was not observed in triethylamine formate TEAF (pH 3.14) buffer solutions.     

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.     

Targeted quantitation of CVD-linked plasma proteins for biomarker verification and validation

Despite significant advances in treatment, cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in developed and developing countries. Judicious monitoring of common risk factors has been unable to control this global epidemic, necessitating novel biomarkers for improved screening and earlier disease detection and management. Although numerous plasma proteins have been associated with CVD, only a few of these potential biomarkers have been validated for clinical use. Here we review the quantitative proteomic methods used to verify and validate new biomarker candidates in human plasma. These methods center on a bottom-up approach involving multiple or selected reaction monitoring, for targeted detection, with stable isotope-labeled standards, for peptide normalization. Also included are a discussion of future strategies for improved CVD protein biomarker verification and validation, recommendations for method translation to the clinic, and future projections for protein biomarker research.     

A proteomic study of the HUPO Plasma Proteome Project's pilot samples using an accurate mass and time tag strategy

Characterization of the human blood plasma proteome is critical to the discovery of routinely useful clinical biomarkers. We used an accurate mass and time (AMT) tag strategy with high-resolution mass accuracy cLC-FT-ICR MS to perform a global proteomic analysis of pilot study samples as part of the HUPO Plasma Proteome Project. HUPO reference serum and citrated plasma samples from African Americans, Asian Americans, and Caucasian Americans were analyzed, in addition to a Pacific Northwest National Laboratory reference serum and plasma. The AMT tag strategy allowed us to leverage two previously published "shotgun" proteomics experiments to perform global analyses on these samples in triplicate in less than 4 days total analysis time. A total of 722 (22% with multiple peptide identifications) International Protein Index redundant proteins, or 377 protein families by ProteinProphet, were identified over the six individual HUPO serum and plasma samples. The samples yielded a similar number of identified redundant proteins in the plasma samples (average 446 +/- 23) as found in the serum samples (average 440 +/- 20). These proteins were identified by an average of 956 +/- 35 unique peptides in plasma and 930 +/- 11 unique peptides in serum. In addition to this high-throughput analysis, the AMT tag approach was used with a Z-score normalization to compare relative protein abundances. This analysis highlighted both known differences in serum and citrated plasma such as fibrinogens, and reproducible differences in peptide abundances from proteins such as soluble activin receptor-like kinase 7b and glycoprotein m6b. The AMT tag strategy not only improved our sample throughput but also provided a basis for estimated quantitation.     

Streamlined Development of Targeted Mass Spectrometry‐Based Method Combining Scout‐MRM and a Web‐Based Tool Indexed with Scout Peptides

MS‐based targeted proteomics is a relevant technology for sensitive and robust relative or absolute quantification of proteins biomarker candidates in complex human biofluids or tissue extracts. Performing a multiplex assay imposes time scheduling of peptide monitoring only around their expected retention time that needs to be defined with synthetic peptide. Time‐scheduled monitoring is clearly a constraint that precludes from straightforward assay transfer between biological matrices or distinct experimental setup. Any unexpected retention time (RT) shift challenges assay robustness and its implementation for large‐scale analysis. Recently, Scout‐multiple reaction monitoring that fully releases multiplexed targeted acquisition from RT scheduling by successively monitoring complex transition groups triggered with sentinel molecules called Scout has been introduced. It is herein documented how Peptide Selector database and tool streamlines the building of a multiplexed method thanks to RT indexation relative to Scout peptides. This case study deals with surrogate peptides of biomarker candidates related to drug‐induced liver and vascular injury, running such on‐line built method (eight Scouts triggering the monitoring of a total of 692 transitions) enables 100% recovery of a panel of 93 spiked‐in heavy labeled standards, despite significant RT shifts between serum, plasma, or urine. This result illustrates the simplicity of automatically building and deploying robust proteomics targeted assay.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

Mass Spectrometric Identification of Proteotypic Peptides from Clinically Used Tumor Markers

Introduction

With the rapid development of mass spectrometry-based technologies such as multiple reaction monitoring and heavy-isotope-labeled-peptide standards, quantitative analysis of biomarker proteins using mass spectrometry is rapidly progressing toward detection of target proteins/peptides from clinical samples. Proteotypic peptides are a few peptides that are repeatedly and consistently identified from a protein in a mixture and are used for quantitative analysis of the protein in a complex biological sample by mass spectrometry.

Materials and Methods

Using mass spectrometry, we identified peptide sequences and provided a list of tryptic peptides and glycopeptides as proteotypic peptides from five clinically used tumor markers, including prostate-specific antigen, carcinoembryonic antigen, Her-2, human chorionic gonadotropin, and CA125.

Conclusion

These proteotypic peptides have potential for targeted detection as well as heavy-isotope-peptide standards for quantitative analysis of marker proteins in clinical specimens using a highly specific, sensitive, and high-throughout mass spectrometry-based analysis method.     

Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring

In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure.     

P23-M Limit of Quantitation for Low-Abundance Proteins in Plasma by Targeted MS

Biomarker discovery results in the creation of candidate lists of potential markers that must be subsequently verified in plasma.¹ The most mature methods at present require abundant protein depletion and fractionation at the protein/peptide levels in order to detect and quantitate low ng/mL concentrations of plasma proteins by stable isotope dilution mass spectrometry. Sample-processing methods with sufficient throughput, recovery, and reproducibility to enable robust detection and quantitation of candidate bio-marker proteins were evaluated by adding five non-native proteins to immunoaffinity-depleted female plasma at varying concentrations (1000, 100, 50, 25, and 10 ng/mL). Each protein was monitored by one or more representative synthetic tryptic peptides labeled with [¹³C₆]leucine or [¹³C₅] valine. Following reduction, carbamidomethylation, and enzymatic digestion, two separate processing paths were compared. In path 1, digested plasma was diluted 1:10 and [¹³C] internal standards were added just prior to direct analysis by multiple reaction monitoring with LC-MS/MS (MRM LC-MS/MS). In path 2, peptides were separated by strong cation exchange, and [¹³C] internal standards were added to corresponding SCX fractions prior to analysis by MRM LC-MS/MS. Detection and quantitation by MRM used the response of at least two product ions from each of the signature peptides. Using processing path 1, we achieved detection and quantitation down to 50 ng/mL in depleted plasma. However, using processing path 2, we achieved detection and quantitation of all spiked proteins, including the non-native protein at 10 ng/mL. While analysis of non-fractionated plasma achieved higher recovery of those proteins detected in both processes, SCX fractionation at the peptide level clearly increases detection and LOQs for potential biomarker proteins in plasma.     

Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.